SparkStreaming结合kafka将offSet保存在redis中

SparkStreaming结合kafka将offSet保存在redis中

SparkStreaming结合kafka的两种方式

1、SparkStreaming的高级APi CreateDStream，容易发生数据多次读取，官方已经不推荐
2、SparkStreaming的低级APi createDirectStream 需要自己保存offset
保存方式有两大类，一类是Spark自带的checkpoint()但是由于其移植性比较差，影响代码升级，因此生产中很少使用。第二类就是将偏移量保存到外部数据库中如redis,hbase,zookeeper,mysql,oracle中，本文将偏移量保存到redis中

kafka消费的两种方式

1、ConsumerStrategies.Subscribe（）自动模式分区自动指定
2、ConsumerStrategies.Assign()手动模式分区手动指定
二者尽量不要混合使用，容易发生问题，生产中尽量使用自动模式
具体差别请参考：
https://www.jianshu.com/p/b09c28d45b82
https://www.cnblogs.com/dongxiao-yang/p/7200971.html

App

package com.xxy.app


import com.xxy.utils.JedisConnPool
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}
import redis.clients.jedis.{Jedis, Transaction}

import scala.collection.JavaConverters._
import scala.collection.mutable
import scala.util.Try
object RedisApp {


  def getOffset(topics: Array[String], groupId: String): mutable.Map[TopicPartition, Long] = {
    //可变的Map，便于后面添加偏移量
    val fromOffset = scala.collection.mutable.Map[TopicPartition, Long]()
    // 获取redis存储的值
    val jedis: Jedis = JedisConnPool.getConnections()
    topics.foreach(topic => {
      val keys = jedis.keys(s"offset_${groupId}_${topic}*")
      if (! keys.isEmpty) {
        keys.asScala.foreach(key => {
          val offset = jedis.get(key)
          val partition = Try(key.split(s"offset_${groupId}_${topic}_").apply(1)).getOrElse("0")
          //输出
          println(partition+":::"+offset)
          fromOffset.put(new TopicPartition(topic, partition.toInt), offset.toLong)
        })
      }
    })
    jedis.close()
    fromOffset
  }

  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("offSet Redis").setMaster("local[2]")
    val context: SparkContext = new SparkContext(conf)
    context.setLogLevel("WARN")
    val ssc: StreamingContext = new StreamingContext(context,Seconds(10))
    //kafka topic
    val topics = Array("offset-redis-01")
    //kafka params
    val kafkaParams: Map[String, Object] = Map(
      "bootstrap.servers" -> "node1:9092,node2:9092,node3:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> "offSet-Redis-Test",
      "auto.offset.reset" -> "latest",
      "enable.auto.commit" -> (false: java.lang.Boolean)
    )
    val groupId: String = kafkaParams.get("group.id").get.toString
    //获取消费方式消费方式有三种
    //earlist:当个分区下有已经提交的offset时候，从提交的offset开始消费，无提交的offset时，从头开始消费
    //latest：当各分区下有已经提交的offset时候，从提交的offset开始消费，无提交的offset时，消费新产生的该分区下的数据
    //none:topic各分区都存在已经提交的offset时，从offset后开始消费，只要有一个分区不存在已提交的offset，则抛出异常
    val reset: String = kafkaParams.get("auto.offset.reset").get.toString
    //获取偏移量
    val offsets = getOffset(topics,groupId);
    //spark读取分方式，均匀分布
    val locationStrategy: LocationStrategy = LocationStrategies.PreferConsistent
    val conumerStrategy: ConsumerStrategy[String, String] = ConsumerStrategies.Subscribe(topics,kafkaParams,offsets)
    //创建directstream
    val kafkaInputStream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream(ssc,locationStrategy,conumerStrategy)
    kafkaInputStream.foreachRDD(
      rdd=>{
        val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
       if (!rdd.isEmpty()) {
         val jedis: Jedis = JedisConnPool.getConnections()
         //开启jedis事务
         val transaction: Transaction = jedis.multi()
         //统计数据
         val result: RDD[(String, Long)] = rdd.map(_.value).flatMap(_.split(" ")).map((_,1L)).reduceByKey(_+_)
         result.foreach(println)
         //重新设置redis值
         offsetRanges.foreach(
           iter=>{
             val key = s"offset_${groupId}_${iter.topic}_${iter.partition}"
             val value = iter.untilOffset
             transaction.set(key,value.toString)
           }
         )
         transaction.exec()
         transaction.close()
         jedis.close()
       }
      }
    )
    ssc.start()
    ssc.awaitTermination()
  }
}

JedisConnPool

package com.xxy.utils

import java.io.FileInputStream
import java.util.Properties

import redis.clients.jedis.{Jedis, JedisPool, JedisPoolConfig}

object JedisConnPool {
  //加载配置文件
  val properties = new Properties()
  val path = Thread.currentThread().getContextClassLoader.getResource("jedis.properties").getPath
  properties.load(new FileInputStream(path))
  val host: String = properties.getProperty("redis.host")
  val auth: String = properties.getProperty("redis.auth")
  val port: Int = properties.getProperty("redis.port").toInt
  //连接配置
  val config = new JedisPoolConfig
  //最大连接数
  config.setMaxTotal(properties.getProperty("redis.maxConn").toInt)
  //最大空闲连接数
  config.setMaxIdle(properties.getProperty("redis.maxIdle").toInt)
  //设置连接池属性分别
  val pool = new JedisPool(config,host,port,10000,auth)
  //连接池
  def getConnections():Jedis = {
    pool.getResource
  }
}